Training & Awareness Policy for AI Governance
Training & Competence ISO/IEC 42001 §7.2 EU/UK aligned
+ On this page
Key takeaways
- Training & awareness are mandatory for an effective AI Management System (AIMS), not “nice-to-have”.
- Competence is defined per role (developers, oversight, leadership, suppliers) with clear evaluation criteria.
- All training must be evidenced with completion records, assessments, and EV-IDs for audit and certification.
Purpose & scope
This Training & Awareness Policy defines how Zen AI Governance designs, delivers, and evidences role-appropriate
training for everyone who influences, develops, deploys, uses, or oversees AI systems under the AI Management
System (AIMS). It applies to permanent employees, contractors, and (where contractually agreed) key suppliers
and service providers.
The policy covers: foundational AI governance training, role-specific technical and risk modules, awareness
campaigns, refresher training, competence assessment, and evidence required for ISO/IEC 42001, EU AI Act,
GDPR/UK GDPR, and UK AI governance expectations.
Guiding principles
- Risk-proportionate: Training depth and cadence increase with the risk of the AI use case.
- Role-specific: Every role has a defined competence profile and learning path.
- Lifecycle-aware: Topics follow the AI lifecycle: design, data, model, deployment, PMM, retirement.
- Evidence-based: Completion and competence are evidenced with records, not assumptions.
- Continuous: Training is an ongoing programme, not a one-off “launch day” exercise.
- Explainable & practical: Content focuses on real workflows, examples, and decisions staff actually make.
Role-based competence framework
The AIMS defines key AI-related roles and their minimum competence requirements. The table below is a
simplified view; detailed matrices can be maintained in the central Training Register.
| Role group | Examples | Key competence areas |
|---|
| AI & Data Engineering | Data Scientists, ML Engineers, Prompt Engineers | Data governance, bias & fairness, evaluation metrics, secure coding, documentation. |
| Product & Business Owners | Product Managers, Service Owners | AI risk scenarios, user journeys, human oversight patterns, acceptance criteria, PMM responsibilities. |
| AI Governance & Risk | AI Risk Officer, Compliance, DPO, Legal Counsel | Frameworks (EU AI Act, ISO 42001, NIST AI RMF), DPIA, risk registers, policies & controls. |
| Human Oversight Operators | Front-line reviewers, safety teams, specialist approvers | Oversight playbooks, escalation, “kill switch”, interpreting AI outputs, fatigue management. |
| Customer-Facing Staff | Support agents, account managers | Transparency notices, handling AI-assisted answers, complaints and issue reporting. |
| Senior Leadership | C-level, Board, senior managers | AI strategy, risk appetite, accountability, reading dashboards, management review responsibilities. |
| Suppliers & Partners | Key model/API vendors, integration partners | Contractual obligations, incident reporting, security & privacy expectations, logging and PMM feeds. |
For each role group, a detailed matrix specifies: required knowledge topics, minimal training hours, assessment
type (quiz, case study, simulation), and renewal frequency.
Core training curriculum
The curriculum is structured into Foundational modules (for all staff) and Role-specific tracks.
Foundational modules (all staff)
- AI Basics & Use at Zen AI Governance: What AI is, where we use it, main risks.
- AI Governance & Our AIMS: Overview of ISO 42001, EU/UK principles, key policies.
- Data Protection & Privacy: GDPR basics, lawful bases, DPIA, data subject rights.
- Security & Acceptable Use: Secure handling of AI tools, prompt hygiene, secret management.
- Transparency & Human Option: How we inform users and offer human review.
- How to Raise AI Concerns: Reporting channels for incidents, bias concerns, or misuse.
Role-specific tracks (examples)
- Engineering track: data lineage, dataset curation, evaluation pipelines, adversarial testing,
logging and monitoring, rollback practices.
- Governance & Risk track: DPIA & AI risk profiles, risk registers, incident classification, audit prep.
- Oversight track: reviewing AI decisions, challenge/override, documenting rationale, stress cases.
- Leadership track: risk appetite, governance structures, reading dashboards, making decisions based on
risk and performance reports.
Awareness programme
Training is supported by an ongoing Awareness Programme to keep AI governance visible and practical in daily work.
- Monthly AI Governance Bulletin: short newsletter with incidents (anonymised), lessons learned,
regulatory updates, and “good practice” examples.
- Quarterly Focus Topics: e.g., “Bias & fairness month”, “Logging & traceability”, “PMM & drift”.
- Micro-learning: 3–5 minute videos or cards embedded into tools (e.g., “before you deploy a new
model, remember…”).
- Internal talks & clinics: office hours with AI governance experts for questions on real projects.
- Visual nudges: posters or intranet tiles reminding staff how to escalate AI incidents or concerns.
Cadence & frequency
- Onboarding: new joiners in relevant roles complete foundational modules within 30 days.
- Annual refresh: mandatory refresher for all staff in-scope, updating on changes in systems and law.
- Role change: when moving into high-impact AI roles, staff must complete the relevant specialist track
before being granted production access.
- Incident-triggered: after SEV-1 or SEV-2 incidents, targeted retraining is provided to affected teams.
- Model / system updates: when significant AI systems change (architecture, purpose, risk profile),
impacted roles complete delta training.
Delivery methods
- E-learning modules: self-paced, tracked via LMS; used for foundational topics.
- Instructor-led sessions: workshops and deep dives for complex risk and governance scenarios.
- Hands-on labs / sandboxes: controlled environments for testing prompts, models, and edge cases.
- Tabletop exercises: scenario-based sessions for incidents, oversight failures, or regulatory requests.
- Micro-learning cards: short, contextual reminders in tools (e.g., in the deployment pipeline UI).
- External certifications (optional): specialised external courses where appropriate.
Competence assessment & certification
For roles with high impact on AI risk, mere attendance is insufficient. Competence is assessed and documented.
- Knowledge checks: short quizzes attached to e-learning modules with a minimum pass score (e.g. 80%).
- Case studies: written or facilitated analysis of realistic AI governance problems.
- Practical assessments: e.g., configuring oversight thresholds, triaging an AI incident, filling a DPIA.
- Observed practice: senior staff observe human oversight actions and sign off competence.
- Certification: key roles (e.g. “AI Oversight Operator”) may have explicit “certified” status with renewal
every 2 years.
Failed assessments trigger re-training and re-testing, with clear guidance and timelines.
Records, EV-IDs & evidence
Training and awareness activities must be fully traceable within the Evidence Repository using EV-IDs.
- Training catalogue: list of all active courses with IDs, descriptions, mapped roles, and frameworks.
- Attendance & completion: logs of which individuals completed which modules and when.
- Assessment results: scores, attempts, pass/fail flags, remediation notes.
- Competence status: current certificates for high-impact roles (e.g., oversight operators).
- Retention: training records retained for at least the lifetime of the system plus regulatory minimums.
Example EV-IDs:
- EV-TRN-001: “AI Governance Fundamentals” e-learning module (catalogue entry).
- EV-TRN-010: Q3 2025 training completion log for AnswerBot project team.
- EV-TRN-022: Oversight Operator certification results for EU high-risk deployment.
Templates & CSV schemas
A) Training catalogue (CSV headers)
Course_ID,Course_Name,Description,Role_Groups,Criticality_Level,Duration_Minutes,Assessment_Type,Framework_Refs
TRN-001,AI Governance Fundamentals,"Intro to AIMS, EU/UK frameworks","All staff",Medium,45,Quiz,"ISO 42001; EU AI Act"
TRN-010,Human Oversight in Practice,"Hands-on oversight scenarios","Oversight Operators, Product Owners",High,90,
Case Study + Simulation,"EU AI Act Art.14"
B) Training completion log (CSV headers)
Record_ID,User_ID,Name,Role_Group,Course_ID,Completion_Date,Score,Status,Evidence_ID
TRNREC-2025-001,U-1042,"Alex Patel","ML Engineer",TRN-001,2025-03-10,92,Passed,EV-TRN-010
TRNREC-2025-002,U-2099,"Jamie Lee","Oversight Operator",TRN-010,2025-03-15,Pass,Certified,EV-TRN-022
C) Competence status register (CSV headers)
User_ID,Name,Role_Group,Competence_Profile,Certified_From,Certified_Until,Status,Evidence_ID
U-2099,"Jamie Lee","Oversight Operator","High-risk AI Oversight",2025-03-15,2027-03-14,Active,EV-TRN-022
Framework alignment
| Framework | Reference | Relevance |
|---|
| ISO/IEC 42001 | §7.2 | Defines competence and training requirements for AI-related roles. |
| EU AI Act | Arts. 9, 14, 17 | Requires appropriate training for staff involved in high-risk AI systems and oversight. |
| NIST AI RMF | Govern • Manage | Emphasises organisational competence, roles, and responsibilities. |
| UK DSIT AI Principles | Accountability & Governance | Encourages clear accountability, training, and awareness for AI deployments. |
Implementation checklist
- ✅ Roles and competence matrices defined and approved.
- ✅ Training catalogue created, with each course mapped to roles and frameworks.
- ✅ LMS or equivalent tracking mechanism in place for completion and assessments.
- ✅ Training and awareness integrated into onboarding, annual cycles, and incident response.
- ✅ Evidence (EV-IDs) linked to audits, risk registers, PMM logs, and management review packs.
© Zen AI Governance UK Ltd • Regulatory Knowledge • v1 20 Nov 2025 • This page is general guidance, not legal advice.